Modeling and Control of Oxygen Transfer in High Purity Oxygen Activated Sludge Process

This paper contains two contributions. First it is shown, in a simulation study using the IAWQ model, that a linear multivariable time-invariant state-space model can be used to predict the ammonium and nitrate concentration in the last aerated zone in a pre-denitrifying activated sludge process. Secondly, using the estimated linear model, a multivariable linear quadratic (LQ) controller is designed and used to control the ammonium and nitrate concentration.

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Multivariable modeling and control of an activated sludge process

Water Science & Technology ◽

10.1016/s0273-1223(98)00370-9 ◽

1998 ◽

Vol 37 (12) ◽

Cited By ~ 7

Keyword(s):

Activated Sludge ◽

Activated Sludge Process ◽

Modeling And Control ◽

And Control

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Modeling and Control of the Activated Sludge Process

ACS Symposium Series - Computer Applications to Chemical Engineering ◽

10.1021/bk-1980-0124.ch021 ◽

1980 ◽

pp. 367-382

Author(s):

GUSTAFF OLSSON

Keyword(s):

Activated Sludge ◽

Activated Sludge Process ◽

Modeling And Control ◽

And Control

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Modeling and Control of the Activated Sludge Process by Use of Autoregressive Model

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)61468-x ◽

1984 ◽

Vol 17 (2) ◽

pp. 3187-3192

Author(s):

M. Hiraoka ◽

K. Tsumura ◽

I. Fujita ◽

T. Kanaya

Keyword(s):

Activated Sludge ◽

Autoregressive Model ◽

Activated Sludge Process ◽

Modeling And Control ◽

And Control

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System Identification and Control of the Activated Sludge Process by Use of a Statistical Model

Water Science & Technology ◽

10.2166/wst.1989.0316 ◽

1989 ◽

Vol 21 (10-11) ◽

pp. 1161-1172 ◽

Cited By ~ 8

Author(s):

M. Hiraoka ◽

K. Tsumura

Keyword(s):

Control System ◽

System Identification ◽

Statistical Model ◽

Activated Sludge ◽

Sewage Treatment Plant ◽

Treatment Plant ◽

Ar Model ◽

Diurnal Changes ◽

Activated Sludge Process ◽

And Control

The authors have been developing a hierarchical control system for the activated sludge process which consists of an upper level system controlling long-term seasonal variations, a control system of intermediate level aiming at optimization of the process and a control system of lower level controlling diurnal changes or hourly fluctuations. The control system using the multi-variable statistical model is one of the most appropriate control systems based on the modern control theory, for applying the lower level control of the activated sludge process. This paper introduces our efforts for developing the reliable data acquisition system, the control experiments applying the AR-model, one of the statistical models which were conducted at a pilot plant and present studies on the system identification and control at a field sewage treatment plant.

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Model Calibration for the High-Purity Oxygen Activated Sludge Process – Algorithm Development and Evaluation

Water Science & Technology ◽

10.2166/wst.1993.0656 ◽

1993 ◽

Vol 28 (11-12) ◽

pp. 163-171 ◽

Cited By ~ 15

Author(s):

Weibo (Weber) Yuan ◽

David Okrent ◽

Michael K. Stenstrom

Keyword(s):

Activated Sludge ◽

High Purity ◽

Model Calibration ◽

Process Model ◽

Model Parameters ◽

Complex Method ◽

Activated Sludge Process ◽

Activated Sludge Model ◽

Kinetic Coefficients ◽

Calibration Algorithm

A model calibration algorithm is developed for the high-purity oxygen activated sludge process (HPO-ASP). The algorithm is evaluated under different conditions to determine the effect of the following factors on the performance of the algorithm: data quality, number of observations, and number of parameters to be estimated. The process model used in this investigation is the first HPO-ASP model based upon the IAWQ (formerly IAWPRC) Activated Sludge Model No. 1. The objective function is formulated as a relative least-squares function and the non-linear, constrained minimization problem is solved by the Complex method. The stoichiometric and kinetic coefficients of the IAWQ activated sludge model are the parameters focused on in this investigation. Observations used are generated numerically but are made close to the observations from a full-scale high-purity oxygen treatment plant. The calibration algorithm is capable of correctly estimating model parameters even if the observations are severely noise-corrupted. The accuracy of estimation deteriorates gradually with the increase of observation errors. The accuracy of calibration improves when the number of observations (n) increases, but the improvement becomes insignificant when n>96. It is also found that there exists an optimal number of parameters that can be rigorously estimated from a given set of information/data. A sensitivity analysis is conducted to determine what parameters to estimate and to evaluate the potential benefits resulted from collecting additional measurements.

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